To resolve the problems of large-scale data and partial overlapping in image clustering

a novel sliding window based multiple-label propagation clustering algorithm is proposed. An undirected graph is constructed in which the vertex is denoted by the image and the edge represents the relation between images weighted by the similarity computed according to the image distance. Then

community detection is performed by a multiple-label propagation based sliding window. Because a sliding window can store multiple labels

each image may obtain one or more labels. Experiments carried out on public networks and images returned by search engines show that our method can find explicit clusters with partial overlapping.